Implementation of machine-learning based query construction and pattern identification through visualization in user interfaces

Abstract

A computer system, computer-implemented method, and computer program product include a processor(s) (executing code) that obtains a data set(s) related to a patient population diagnosed with a medical condition a database(s). The processor(s) identifies common features, generates patterns of the common features, and generates machine learning algorithms based on the patterns to identify presence or absence of the given medical condition in an undiagnosed patient. The processor(s) compiles a training set of data and tunes the machine learning algorithms with the training set of data. The processor(s) integrates the machine learning algorithms into a graphical user interface. The processor(s) obtains data related to the undiagnosed patient via the interface and applies the machine learning algorithms to determine a probability (numerical value indicating a percentage of commonality between the data related to the undiagnosed patient and the one or more patterns) and display the probability as a score in the interface.

Description

CROSS REFERENCE TO RELATED APPLICATION[0001]This application claims priority to U.S. Provisional Application No. 62 / 783,155 filed Dec. 20, 2018, entitled, “IMPLEMENTATION OF MACHINE-LEARNING BASED QUERY CONSTRUCTION AND PATTERN IDENTIFICATION THROUGH VISUALIZATION IN USER INTERFACES” which is incorporated herein by reference in its entirety.BACKGROUND OF INVENTION[0002]Health patterns indicative of certain health conditions are often difficult to identify. This is true for diseases and medical conditions that are readily known to the general population, as well as with diseases that are so rare that they affect only a small portion of the population.[0003]Some diseases, although known to the general public, are clinically diagnosed through exclusion. Thus, a diagnosis of the disease can be delayed as each other possibility is systematically excluded. This prolonged diagnostic time can be detrimental as it delays initiating approved treatments and the progression of the disease for a...

AI Technical Summary

Benefits of technology

The patent text describes a method and system for determining the likelihood of a medical condition in a patient based on data sets related to the patient. The method involves identifying common features in the data sets, generating patterns based on the common features, and using statistical sampling to create a training set of data. The system then applies machine learning algorithms to the training set of data to determine the probability of the medical condition in an undiagnosed patient. The technical effects of this invention include improved accuracy in predicting medical conditions and providing a score to users based on the likelihood of the condition.

Problems solved by technology

Health patterns indicative of certain health conditions are often difficult to identify.

This prolonged diagnostic time can be detrimental as it delays initiating approved treatments and the progression of the disease for an undiagnosed patient can preclude that patient, when finally diagnosed, from enrolling in a clinical trial and / or a given therapy not having any effect, since the disease can have progressed to a state where the therapy is no longer effective.

Most of these diseases are genetic, frequently misdiagnosed for years, and without FDA-approved drug treatment.

The problem of finding potentially undiagnosed subjects for orphan diseases is that active surveillance for such conditions (canvassing a segment of population with questionnaires and / or tests) is expensive and impractical for rare (or even not so rare) diseases, and passive surveillance has to rely on existing medical records (produced by hospitals and insurance companies), which can be incomplete, unreliable, and not contain enough information relevant for the predictive diagnostics.

Challenges in identifying these orphan diseases from population-related data exist based on both the limitations of present computing solutions to process the volume of data efficiently and the lack of knowledge regarding what parameters should be searched within this large volume.

The challenges related to establishing patterns that identify an event in a large volume of data and actually identifying that event in this large volume are not unique to disease or to orphan disease identification.

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